information Review The Current Role of Image Compression Standards in Medical Imaging Feng Liu 1 ID , Miguel Hernandez-Cabronero 2, Victor Sanchez 3, Michael W. Marcellin 2 and Ali Bilgin 2,4,5,* 1 College of Electronic Information and Optical Engineering, Nankai University, Haihe Education Park, 38 Tongyan Road, Jinnan District, Tianjin 300350, China; [email protected] 2 Department of Electrical and Computer Engineering, The University of Arizona, 1230 E. Speedway Blvd, Tucson, AZ 85721, USA; [email protected] (M.H.-C.); [email protected] (M.W.M.) 3 Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK; [email protected] 4 Department of Biomedical Engineering, The University of Arizona, 1127 E. James E. Rogers Way, Tucson, AZ 85721, USA 5 Department of Medical Imaging, The University of Arizona, 1501 N. Campbell Ave., Tucson, AZ 85724, USA * Correspondence: [email protected]; Tel.: +1-520-626-9414 Received: 25 September 2017; Accepted: 15 October 2017; Published: 19 October 2017 Abstract: With the increasing utilization of medical imaging in clinical practice and the growing dimensions of data volumes generated by various medical imaging modalities, the distribution, storage, and management of digital medical image data sets requires data compression. Over the past few decades, several image compression standards have been proposed by international standardization organizations. This paper discusses the current status of these image compression standards in medical imaging applications together with some of the legal and regulatory issues surrounding the use of compression in medical settings. Keywords: image compression; medical imaging; standards; JPEG; JPEG-LS; JPEG2000; JPEG-XR; HEVC; DICOM 1. Introduction Medical imaging has become an indispensable tool in clinical practice. Studies have shown links between the use of medical imaging exams and declines in mortality, reduced need for exploratory surgery, fewer hospital admissions, shorter hospital stays, and longer life expectancy [1]. As a result, the utilization of medical imaging has risen sharply during the early part of the last decade. In 2003, the percentage of medical visits in the US by patients aged ≥ 65 years that resulted in medical imaging was estimated to be 12.8% [2]. While earlier medical imaging exams were recorded on radiological film, most exams are now acquired digitally. In addition to increased utilization, there have also been major advances in medical imaging technology that have resulted in significant increases in the quantity of digital medical imaging data during the last few decades. For example, in early 1990s, a typical Computed Tomography (CT) exam of the thorax would have consisted of 25 slices with 10 mm thickness, yielding a data size of roughly 12 megabytes (MBs). Today, a similar exam on a modern CT scanner can yield sub-millimeter slice thickness with increased in-plane resolution resulting in 600 MB to a gigabyte (GB) of data. In a modern hospital, Picture Archiving and Communication Systems (PACS) handle the short- and long-term storage, retrieval, management, distribution, processing, and presentation of these large datasets. Data compression plays an important role in these systems. Since the earliest days of PACS, compression of medical images has been anticipated and novel compression techniques have been proposed before standardized compression Information 2017, 8, 131; doi:10.3390/info8040131 www.mdpi.com/journal/information Information 2017, 8, 131 2 of 26 approaches were available [3]. However, proprietary compression techniques greatly increase the cost and effort required to migrate data between different systems, and interoperability and compatibility of these systems necessitate the use of standards for digital communications [4]. In this paper, we provide a review of the current status of image compression standards used for medical imaging data in these systems (It is also worthwhile to point out that the role of data compression in the medical setting is not limited to images. Modern medical practice utilizes many physiological signals (e.g., electrocardiogram (ECG), electroencephalogram (EEG), and Electromyogram (EMG)) which must be stored and transmitted in clinical practice. Data compression has an important role to play for management of such physiological signals as well. However, in this paper, we limit our discussion to medical images). It is important to note that there have been earlier reviews of medical image compression techniques [5–14]. In this paper, we focus on the image compression standards including the more recent standards that have not been considered in these earlier reviews. We also compare the compression performances of these standards on publicly available datasets. The rest of this paper is organized as follows: In Section2, we discuss characteristics of common medical imaging data sets. In Section3, we provide a brief introduction to current image compression standards. Section4 introduces the standards used for medical image communications. Section5 briefly reviews the current legal and regulatory environment in medical image communications. We present experimental results obtained using the image compression standards on typical medical image datasets in Section6. Finally, Section7 provides a summary and a brief discussion on future trends. 2. Characteristics of Medical Imaging Data Sets The purpose of this section is to briefly describe the unique attributes of the major medical imaging modalities. Medical imaging refers to techniques used to view the human body with the goal of diagnosing, monitoring, and/or treating medical conditions. Different imaging modalities are based on different physical principles and provide different information about structure, morphology, and function of the human body. Medical imaging is an active field with new imaging modalities introduced frequently and existing modalities refined and expanded constantly. Given this depth and diversity of the medical imaging field, it is not realistic to provide a complete description of all medical imaging techniques in this section. Therefore, we limit our discussion to the most common medical imaging methods in clinical practice and their general characteristics as they relate to data compression (For an extended review of the characteristics of medical datasets, the interested reader is referred to [15]). Table1 provides the typical image dimensions and uncompressed file sizes for common medical imaging modalities. Table 1. Typical image dimensions and uncompressed file sizes for common medical imaging modalities. Modality Anatomy Image Dimensions (x, y, z, t) Bit Depth Uncompressed File Size Radiography Chest (2000, 2500, -, -) 10–16 bits 10 MB Abdomen (512, 512, 500, -) 12–16 bits 250 MB CT Brain (512, 512, 300, -) 12-16 bits 150 MB Heart (512, 512, 100, 20) 12-16 bits 1 GB Breast Tomosynthesis Breast (2457, 1890, 50, -) 10–16 bits 0.4 GB Abdomen (512, 512, 100, -) 12–16 bits 50 MB MRI Brain (512, 512, 200, -) 12–16 bits 100 MB Heart (256, 256, 20, 25) 12–16 bits 250 MB Ultrasound Heart (512, 512, -, 50)/s 24 bits (color) 38 MB/s Brain (256, 256, 50, -) 16 bits 6 MB PET Heart (128, 128, 40, 16) 16 bits 1 MB Digital Pathology Cells (30,000, 30,000, -, -) 24 bits (color) 2.5 GB Information 2017, 8, 131 3 of 26 2.1. Digital Radiography and Computed Tomography In X-ray radiography, the subject is penetrated by a collimated beam of X-rays and the properties of the X-ray (intensity, energy spectrum, direction of propagation, etc.) are modified as it travels through the human body. An array of X-ray detectors placed behind the subject is then used to record the modified properties of the X-ray beam and form the X-ray image. The dimensions of the image depend on the dimensions of the detector elements (0.1 mm to 0.2 mm) and the field of view (18 × 20 cm to 35 × 40 cm based on the anatomy of interest). A sample X-ray image of the pelvis is shown in Figure1a. In X-ray radiography, a single projection plane of an anatomical region is imaged onto the detector plane. In contrast, CT imaging produces a 3D image of the anatomy of interest by acquiring several projections at different angles and reconstructing the 3D volume using tomography [16]. Today’s multi-detector row CT scanners can acquire up to 320 simultaneous slices in each rotation of the X-ray tube. A thin-slice CT dataset can contain over 500 slices and dynamic imaging (such as cardiac angiography or perfusion imaging) can be performed on the latest generation of CT scanners although the routine clinical utilization of these studies is limited due to concerns about radiation dose. Another recent X-ray imaging technique developed to enhance characterization of breast lesions is Digital Breast Tomosynthesis (3D Mammography) [17,18]. In this technique, thin sections of the breast tissue are reconstructed through acquisition of multiple projections over a limited arc angle. These use of thin slices covering the entire breast allows better visualization and characterization of masses that may otherwise be superimposed with out-of-plane structures. A sample CT image of an axial slice of the abdomen is shown in Figure1b. The contrast resolution in CT is largely determined by the number of photons per voxel and, therefore, there is a trade-off between X-ray dose and contrast resolution, as well as spatial resolution. Noise in CT images originates from a number of sources including photon noise at the detector, electronic noise of the detector system, and the image reconstruction algorithm used. (a) (b) Figure 1. (a) An X-ray image of the pelvis; (b) A CT image of the abdomen. 2.2. Magnetic Resonance Imaging Magnetic Resonance Imaging (MRI) [19] is based on the principles of nuclear magnetic resonance. In MRI, the contrast of the image is dependent on several tissue-dependent parameters as well as the choice of acquisition parameters selected during the imaging procedure. By varying these parameters, MRI can yield images with drastically different contrast.